Overview

Dataset statistics

Number of variables21
Number of observations41180
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory6.6 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
pdays is highly overall correlated with previous and 1 other fieldsHigh correlation
previous is highly overall correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
cons.price.idx is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
cons.conf.idx is highly overall correlated with monthHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 3 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
contact is highly overall correlated with cons.price.idx and 2 other fieldsHigh correlation
month is highly overall correlated with emp.var.rate and 5 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 1 other fieldsHigh correlation
default is highly imbalanced (53.3%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (56.8%)Imbalance
previous has 35559 (86.4%) zerosZeros

Reproduction

Analysis started2023-09-02 07:10:11.678217
Analysis finished2023-09-02 07:10:44.659128
Duration32.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02171
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:44.831127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.419593
Coefficient of variation (CV)0.26034853
Kurtosis0.79161607
Mean40.02171
Median Absolute Deviation (MAD)7
Skewness0.78461743
Sum1648094
Variance108.56793
MonotonicityNot monotonic
2023-09-02T12:55:45.054128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1947
 
4.7%
32 1846
 
4.5%
33 1833
 
4.5%
36 1780
 
4.3%
35 1759
 
4.3%
34 1745
 
4.2%
30 1714
 
4.2%
37 1474
 
3.6%
29 1453
 
3.5%
39 1432
 
3.5%
Other values (68) 24197
58.8%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 28
 
0.1%
19 42
 
0.1%
20 65
 
0.2%
21 102
 
0.2%
22 137
 
0.3%
23 226
 
0.5%
24 463
1.1%
25 598
1.5%
26 698
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 22
0.1%
87 1
 
< 0.1%
86 8
 
< 0.1%
85 15
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
admin.
10422 
blue-collar
9253 
technician
6742 
services
3967 
management
2923 
Other values (7)
7873 

Length

Max length13
Median length12
Mean length8.9552695
Min length6

Characters and Unicode

Total characters368778
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowblue-collar

Common Values

ValueCountFrequency (%)
admin. 10422
25.3%
blue-collar 9253
22.5%
technician 6742
16.4%
services 3967
 
9.6%
management 2923
 
7.1%
retired 1718
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1059
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Length

2023-09-02T12:55:45.272127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 10422
25.3%
blue-collar 9253
22.5%
technician 6742
16.4%
services 3967
 
9.6%
management 2923
 
7.1%
retired 1718
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1059
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 47260
12.8%
n 35543
 
9.6%
a 33322
 
9.0%
l 31615
 
8.6%
i 30650
 
8.3%
c 26704
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16509
 
4.5%
t 14589
 
4.0%
Other values (14) 91800
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 347682
94.3%
Dash Punctuation 10674
 
2.9%
Other Punctuation 10422
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 47260
13.6%
n 35543
10.2%
a 33322
9.6%
l 31615
9.1%
i 30650
8.8%
c 26704
 
7.7%
r 21024
 
6.0%
m 19762
 
5.7%
d 16509
 
4.7%
t 14589
 
4.2%
Other values (12) 70704
20.3%
Dash Punctuation
ValueCountFrequency (%)
- 10674
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 347682
94.3%
Common 21096
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 47260
13.6%
n 35543
10.2%
a 33322
9.6%
l 31615
9.1%
i 30650
8.8%
c 26704
 
7.7%
r 21024
 
6.0%
m 19762
 
5.7%
d 16509
 
4.7%
t 14589
 
4.2%
Other values (12) 70704
20.3%
Common
ValueCountFrequency (%)
- 10674
50.6%
. 10422
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 47260
12.8%
n 35543
 
9.6%
a 33322
 
9.0%
l 31615
 
8.6%
i 30650
 
8.3%
c 26704
 
7.2%
r 21024
 
5.7%
m 19762
 
5.4%
d 16509
 
4.5%
t 14589
 
4.0%
Other values (14) 91800
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
married
24921 
single
11568 
divorced
4611 
unknown
 
80

Length

Max length8
Median length7
Mean length6.8310588
Min length6

Characters and Unicode

Total characters281303
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 24921
60.5%
single 11568
28.1%
divorced 4611
 
11.2%
unknown 80
 
0.2%

Length

2023-09-02T12:55:45.465127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:45.645128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 24921
60.5%
single 11568
28.1%
divorced 4611
 
11.2%
unknown 80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 54453
19.4%
i 41100
14.6%
e 41100
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 281303
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 54453
19.4%
i 41100
14.6%
e 41100
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281303
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 54453
19.4%
i 41100
14.6%
e 41100
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14153
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 54453
19.4%
i 41100
14.6%
e 41100
14.6%
d 34143
12.1%
m 24921
8.9%
a 24921
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14153
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
university.degree
12166 
high.school
9513 
basic.9y
6044 
professional.course
5241 
basic.4y
4175 
Other values (3)
4041 

Length

Max length19
Median length17
Mean length12.710758
Min length7

Characters and Unicode

Total characters523429
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.6y
2nd rowhigh.school
3rd rowbasic.9y
4th rowprofessional.course
5th rowunknown

Common Values

ValueCountFrequency (%)
university.degree 12166
29.5%
high.school 9513
23.1%
basic.9y 6044
14.7%
professional.course 5241
12.7%
basic.4y 4175
 
10.1%
basic.6y 2292
 
5.6%
unknown 1731
 
4.2%
illiterate 18
 
< 0.1%

Length

2023-09-02T12:55:45.835177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:46.021128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 12166
29.5%
high.school 9513
23.1%
basic.9y 6044
14.7%
professional.course 5241
12.7%
basic.4y 4175
 
10.1%
basic.6y 2292
 
5.6%
unknown 1731
 
4.2%
illiterate 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 59182
 
11.3%
i 51633
 
9.9%
s 49913
 
9.5%
. 39431
 
7.5%
o 36480
 
7.0%
r 34832
 
6.7%
h 28539
 
5.5%
c 27265
 
5.2%
y 24677
 
4.7%
n 22600
 
4.3%
Other values (15) 148877
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 471487
90.1%
Other Punctuation 39431
 
7.5%
Decimal Number 12511
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 59182
12.6%
i 51633
11.0%
s 49913
10.6%
o 36480
 
7.7%
r 34832
 
7.4%
h 28539
 
6.1%
c 27265
 
5.8%
y 24677
 
5.2%
n 22600
 
4.8%
g 21679
 
4.6%
Other values (11) 114687
24.3%
Decimal Number
ValueCountFrequency (%)
9 6044
48.3%
4 4175
33.4%
6 2292
 
18.3%
Other Punctuation
ValueCountFrequency (%)
. 39431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 471487
90.1%
Common 51942
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 59182
12.6%
i 51633
11.0%
s 49913
10.6%
o 36480
 
7.7%
r 34832
 
7.4%
h 28539
 
6.1%
c 27265
 
5.8%
y 24677
 
5.2%
n 22600
 
4.8%
g 21679
 
4.6%
Other values (11) 114687
24.3%
Common
ValueCountFrequency (%)
. 39431
75.9%
9 6044
 
11.6%
4 4175
 
8.0%
6 2292
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 523429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 59182
 
11.3%
i 51633
 
9.9%
s 49913
 
9.5%
. 39431
 
7.5%
o 36480
 
7.0%
r 34832
 
6.7%
h 28539
 
5.5%
c 27265
 
5.2%
y 24677
 
4.7%
n 22600
 
4.3%
Other values (15) 148877
28.4%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
32581 
unknown
8596 
yes
 
3

Length

Max length7
Median length2
Mean length3.0437834
Min length2

Characters and Unicode

Total characters125343
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowunknown
4th rowno
5th rowunknown

Common Values

ValueCountFrequency (%)
no 32581
79.1%
unknown 8596
 
20.9%
yes 3
 
< 0.1%

Length

2023-09-02T12:55:46.236128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:46.389127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 32581
79.1%
unknown 8596
 
20.9%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 58369
46.6%
o 41177
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125343
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 58369
46.6%
o 41177
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 125343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 58369
46.6%
o 41177
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 58369
46.6%
o 41177
32.9%
u 8596
 
6.9%
k 8596
 
6.9%
w 8596
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
yes
21571 
no
18619 
unknown
 
990

Length

Max length7
Median length3
Mean length2.6440262
Min length2

Characters and Unicode

Total characters108881
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 21571
52.4%
no 18619
45.2%
unknown 990
 
2.4%

Length

2023-09-02T12:55:46.552128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:46.703173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 21571
52.4%
no 18619
45.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 21589
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19609
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108881
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 21589
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19609
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 108881
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 21589
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19609
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 21589
19.8%
y 21571
19.8%
e 21571
19.8%
s 21571
19.8%
o 19609
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
33943 
yes
6247 
unknown
 
990

Length

Max length7
Median length2
Mean length2.2719038
Min length2

Characters and Unicode

Total characters93557
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 33943
82.4%
yes 6247
 
15.2%
unknown 990
 
2.4%

Length

2023-09-02T12:55:46.869128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:47.020128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 33943
82.4%
yes 6247
 
15.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 36913
39.5%
o 34933
37.3%
y 6247
 
6.7%
e 6247
 
6.7%
s 6247
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93557
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36913
39.5%
o 34933
37.3%
y 6247
 
6.7%
e 6247
 
6.7%
s 6247
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 93557
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36913
39.5%
o 34933
37.3%
y 6247
 
6.7%
e 6247
 
6.7%
s 6247
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36913
39.5%
o 34933
37.3%
y 6247
 
6.7%
e 6247
 
6.7%
s 6247
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

contact
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
cellular
26140 
telephone
15040 

Length

Max length9
Median length8
Mean length8.3652258
Min length8

Characters and Unicode

Total characters344480
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 26140
63.5%
telephone 15040
36.5%

Length

2023-09-02T12:55:47.189128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:47.341172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 26140
63.5%
telephone 15040
36.5%

Most occurring characters

ValueCountFrequency (%)
l 93460
27.1%
e 71260
20.7%
c 26140
 
7.6%
u 26140
 
7.6%
a 26140
 
7.6%
r 26140
 
7.6%
t 15040
 
4.4%
p 15040
 
4.4%
h 15040
 
4.4%
o 15040
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 344480
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 93460
27.1%
e 71260
20.7%
c 26140
 
7.6%
u 26140
 
7.6%
a 26140
 
7.6%
r 26140
 
7.6%
t 15040
 
4.4%
p 15040
 
4.4%
h 15040
 
4.4%
o 15040
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 344480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 93460
27.1%
e 71260
20.7%
c 26140
 
7.6%
u 26140
 
7.6%
a 26140
 
7.6%
r 26140
 
7.6%
t 15040
 
4.4%
p 15040
 
4.4%
h 15040
 
4.4%
o 15040
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 93460
27.1%
e 71260
20.7%
c 26140
 
7.6%
u 26140
 
7.6%
a 26140
 
7.6%
r 26140
 
7.6%
t 15040
 
4.4%
p 15040
 
4.4%
h 15040
 
4.4%
o 15040
 
4.4%

month
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
may
13765 
jul
7174 
aug
6178 
jun
5318 
nov
4097 
Other values (5)
4648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123540
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13765
33.4%
jul 7174
17.4%
aug 6178
15.0%
jun 5318
 
12.9%
nov 4097
 
9.9%
apr 2632
 
6.4%
oct 718
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Length

2023-09-02T12:55:47.494127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:47.676127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 13765
33.4%
jul 7174
17.4%
aug 6178
15.0%
jun 5318
 
12.9%
nov 4097
 
9.9%
apr 2632
 
6.4%
oct 718
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 23121
18.7%
u 18670
15.1%
m 14311
11.6%
y 13765
11.1%
j 12492
10.1%
n 9415
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4815
 
3.9%
v 4097
 
3.3%
Other values (7) 9502
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23121
18.7%
u 18670
15.1%
m 14311
11.6%
y 13765
11.1%
j 12492
10.1%
n 9415
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4815
 
3.9%
v 4097
 
3.3%
Other values (7) 9502
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 123540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23121
18.7%
u 18670
15.1%
m 14311
11.6%
y 13765
11.1%
j 12492
10.1%
n 9415
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4815
 
3.9%
v 4097
 
3.3%
Other values (7) 9502
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 23121
18.7%
u 18670
15.1%
m 14311
11.6%
y 13765
11.1%
j 12492
10.1%
n 9415
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4815
 
3.9%
v 4097
 
3.3%
Other values (7) 9502
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
thu
8622 
mon
8509 
wed
8133 
tue
8090 
fri
7826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123540
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu 8622
20.9%
mon 8509
20.7%
wed 8133
19.7%
tue 8090
19.6%
fri 7826
19.0%

Length

2023-09-02T12:55:47.888128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:48.048619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 8622
20.9%
mon 8509
20.7%
wed 8133
19.7%
tue 8090
19.6%
fri 7826
19.0%

Most occurring characters

ValueCountFrequency (%)
t 16712
13.5%
u 16712
13.5%
e 16223
13.1%
h 8622
7.0%
m 8509
6.9%
o 8509
6.9%
n 8509
6.9%
w 8133
6.6%
d 8133
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123540
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 16712
13.5%
u 16712
13.5%
e 16223
13.1%
h 8622
7.0%
m 8509
6.9%
o 8509
6.9%
n 8509
6.9%
w 8133
6.6%
d 8133
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 123540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 16712
13.5%
u 16712
13.5%
e 16223
13.1%
h 8622
7.0%
m 8509
6.9%
o 8509
6.9%
n 8509
6.9%
w 8133
6.6%
d 8133
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 16712
13.5%
u 16712
13.5%
e 16223
13.1%
h 8622
7.0%
m 8509
6.9%
o 8509
6.9%
n 8509
6.9%
w 8133
6.6%
d 8133
6.6%
f 7826
6.3%
Other values (2) 15652
12.7%

duration
Real number (ℝ)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.28043
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:48.252615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.29986
Coefficient of variation (CV)1.003947
Kurtosis20.245266
Mean258.28043
Median Absolute Deviation (MAD)94
Skewness3.2630242
Sum10635988
Variance67236.415
MonotonicityNot monotonic
2023-09-02T12:55:48.465620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 170
 
0.4%
90 170
 
0.4%
136 168
 
0.4%
73 167
 
0.4%
124 164
 
0.4%
87 162
 
0.4%
104 161
 
0.4%
72 161
 
0.4%
111 160
 
0.4%
106 159
 
0.4%
Other values (1534) 39538
96.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 12
 
< 0.1%
5 30
 
0.1%
6 37
0.1%
7 54
0.1%
8 69
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
4199 1
< 0.1%
3785 1
< 0.1%
3643 1
< 0.1%
3631 1
< 0.1%
3509 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%

campaign
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5677999
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:48.655572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7702251
Coefficient of variation (CV)1.0788322
Kurtosis36.973911
Mean2.5677999
Median Absolute Deviation (MAD)1
Skewness4.7621385
Sum105742
Variance7.6741471
MonotonicityNot monotonic
2023-09-02T12:55:48.844490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 17637
42.8%
2 10568
25.7%
3 5340
 
13.0%
4 2651
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
Other values (32) 869
 
2.1%
ValueCountFrequency (%)
1 17637
42.8%
2 10568
25.7%
3 5340
 
13.0%
4 2651
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.51671
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:49.026536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.80903
Coefficient of variation (CV)0.19408393
Kurtosis22.260074
Mean962.51671
Median Absolute Deviation (MAD)0
Skewness-4.9252983
Sum39636438
Variance34897.613
MonotonicityNot monotonic
2023-09-02T12:55:49.199539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
999 39667
96.3%
3 439
 
1.1%
6 411
 
1.0%
4 118
 
0.3%
9 64
 
0.2%
2 61
 
0.1%
7 60
 
0.1%
12 58
 
0.1%
10 52
 
0.1%
5 46
 
0.1%
Other values (17) 204
 
0.5%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 25
 
0.1%
2 61
 
0.1%
3 439
1.1%
4 118
 
0.3%
5 46
 
0.1%
6 411
1.0%
7 60
 
0.1%
8 18
 
< 0.1%
9 64
 
0.2%
ValueCountFrequency (%)
999 39667
96.3%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
19 3
 
< 0.1%
18 7
 
< 0.1%
17 8
 
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1727052
Minimum0
Maximum7
Zeros35559
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:49.349536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49371879
Coefficient of variation (CV)2.8587373
Kurtosis19.78413
Mean0.1727052
Median Absolute Deviation (MAD)0
Skewness3.8104936
Sum7112
Variance0.24375824
MonotonicityNot monotonic
2023-09-02T12:55:49.507540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35559
86.4%
1 4559
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 69
 
0.2%
5 18
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 35559
86.4%
1 4559
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 69
 
0.2%
5 18
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 4
 
< 0.1%
5 18
 
< 0.1%
4 69
 
0.2%
3 216
 
0.5%
2 754
 
1.8%
1 4559
 
11.1%
0 35559
86.4%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
nonexistent
35559 
failure
4250 
success
 
1371

Length

Max length11
Median length11
Mean length10.454007
Min length7

Characters and Unicode

Total characters430496
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 35559
86.4%
failure 4250
 
10.3%
success 1371
 
3.3%

Length

2023-09-02T12:55:49.706958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-02T12:55:49.879017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 35559
86.4%
failure 4250
 
10.3%
success 1371
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 106677
24.8%
e 76739
17.8%
t 71118
16.5%
i 39809
 
9.2%
s 39672
 
9.2%
o 35559
 
8.3%
x 35559
 
8.3%
u 5621
 
1.3%
f 4250
 
1.0%
a 4250
 
1.0%
Other values (3) 11242
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 430496
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 106677
24.8%
e 76739
17.8%
t 71118
16.5%
i 39809
 
9.2%
s 39672
 
9.2%
o 35559
 
8.3%
x 35559
 
8.3%
u 5621
 
1.3%
f 4250
 
1.0%
a 4250
 
1.0%
Other values (3) 11242
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 430496
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 106677
24.8%
e 76739
17.8%
t 71118
16.5%
i 39809
 
9.2%
s 39672
 
9.2%
o 35559
 
8.3%
x 35559
 
8.3%
u 5621
 
1.3%
f 4250
 
1.0%
a 4250
 
1.0%
Other values (3) 11242
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 106677
24.8%
e 76739
17.8%
t 71118
16.5%
i 39809
 
9.2%
s 39672
 
9.2%
o 35559
 
8.3%
x 35559
 
8.3%
u 5621
 
1.3%
f 4250
 
1.0%
a 4250
 
1.0%
Other values (3) 11242
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081901408
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17187
Negative (%)41.7%
Memory size321.8 KiB
2023-09-02T12:55:50.163998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5710371
Coefficient of variation (CV)19.182052
Kurtosis-1.0626557
Mean0.081901408
Median Absolute Deviation (MAD)0.3
Skewness-0.7241447
Sum3372.7
Variance2.4681576
MonotonicityNot monotonic
2023-09-02T12:55:50.310998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 16234
39.4%
-1.8 9184
22.3%
1.1 7759
18.8%
-0.1 3683
 
8.9%
-2.9 1663
 
4.0%
-3.4 1071
 
2.6%
-1.7 773
 
1.9%
-1.1 631
 
1.5%
-3 172
 
0.4%
-0.2 10
 
< 0.1%
ValueCountFrequency (%)
-3.4 1071
 
2.6%
-3 172
 
0.4%
-2.9 1663
 
4.0%
-1.8 9184
22.3%
-1.7 773
 
1.9%
-1.1 631
 
1.5%
-0.2 10
 
< 0.1%
-0.1 3683
 
8.9%
1.1 7759
18.8%
1.4 16234
39.4%
ValueCountFrequency (%)
1.4 16234
39.4%
1.1 7759
18.8%
-0.1 3683
 
8.9%
-0.2 10
 
< 0.1%
-1.1 631
 
1.5%
-1.7 773
 
1.9%
-1.8 9184
22.3%
-2.9 1663
 
4.0%
-3 172
 
0.4%
-3.4 1071
 
2.6%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.575508
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:50.467003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57876248
Coefficient of variation (CV)0.0061849782
Kurtosis-0.83024371
Mean93.575508
Median Absolute Deviation (MAD)0.38
Skewness-0.2310988
Sum3853439.4
Variance0.33496601
MonotonicityNot monotonic
2023-09-02T12:55:50.638999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 7759
18.8%
93.918 6685
16.2%
92.893 5794
14.1%
93.444 5175
12.6%
94.465 4374
10.6%
93.2 3616
8.8%
93.075 2458
 
6.0%
92.201 770
 
1.9%
92.963 715
 
1.7%
92.431 447
 
1.1%
Other values (16) 3387
8.2%
ValueCountFrequency (%)
92.201 770
 
1.9%
92.379 267
 
0.6%
92.431 447
 
1.1%
92.469 178
 
0.4%
92.649 357
 
0.9%
92.713 172
 
0.4%
92.756 10
 
< 0.1%
92.843 282
 
0.7%
92.893 5794
14.1%
92.963 715
 
1.7%
ValueCountFrequency (%)
94.767 124
 
0.3%
94.601 204
 
0.5%
94.465 4374
10.6%
94.215 311
 
0.8%
94.199 303
 
0.7%
94.055 229
 
0.6%
94.027 233
 
0.6%
93.994 7759
18.8%
93.918 6685
16.2%
93.876 212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.501999
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41180
Negative (%)100.0%
Memory size321.8 KiB
2023-09-02T12:55:50.804955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6273579
Coefficient of variation (CV)-0.11425011
Kurtosis-0.35872693
Mean-40.501999
Median Absolute Deviation (MAD)4.4
Skewness0.30401659
Sum-1667872.3
Variance21.412441
MonotonicityNot monotonic
2023-09-02T12:55:50.977998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 7759
18.8%
-42.7 6685
16.2%
-46.2 5794
14.1%
-36.1 5175
12.6%
-41.8 4374
10.6%
-42 3616
8.8%
-47.1 2458
 
6.0%
-31.4 770
 
1.9%
-40.8 715
 
1.7%
-26.9 447
 
1.1%
Other values (16) 3387
8.2%
ValueCountFrequency (%)
-50.8 124
 
0.3%
-50 282
 
0.7%
-49.5 204
 
0.5%
-47.1 2458
 
6.0%
-46.2 5794
14.1%
-45.9 10
 
< 0.1%
-42.7 6685
16.2%
-42 3616
8.8%
-41.8 4374
10.6%
-40.8 715
 
1.7%
ValueCountFrequency (%)
-26.9 447
 
1.1%
-29.8 267
 
0.6%
-30.1 357
 
0.9%
-31.4 770
 
1.9%
-33 172
 
0.4%
-33.6 178
 
0.4%
-34.6 174
 
0.4%
-34.8 264
 
0.6%
-36.1 5175
12.6%
-36.4 7759
18.8%

euribor3m
Real number (ℝ)

HIGH CORRELATION 

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6214223
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:51.173000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7343854
Coefficient of variation (CV)0.47892381
Kurtosis-1.4065576
Mean3.6214223
Median Absolute Deviation (MAD)0.108
Skewness-0.70934118
Sum149130.17
Variance3.0080926
MonotonicityNot monotonic
2023-09-02T12:55:51.380157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2864
 
7.0%
4.962 2613
 
6.3%
4.963 2487
 
6.0%
4.961 1902
 
4.6%
4.856 1210
 
2.9%
4.964 1175
 
2.9%
1.405 1169
 
2.8%
4.965 1071
 
2.6%
4.864 1044
 
2.5%
4.96 1013
 
2.5%
Other values (306) 24632
59.8%
ValueCountFrequency (%)
0.634 8
 
< 0.1%
0.635 43
0.1%
0.636 14
 
< 0.1%
0.637 6
 
< 0.1%
0.638 7
 
< 0.1%
0.639 16
 
< 0.1%
0.64 10
 
< 0.1%
0.642 35
0.1%
0.643 23
0.1%
0.644 38
0.1%
ValueCountFrequency (%)
5.045 9
 
< 0.1%
5 7
 
< 0.1%
4.97 172
 
0.4%
4.968 992
 
2.4%
4.967 643
 
1.6%
4.966 622
 
1.5%
4.965 1071
2.6%
4.964 1175
2.9%
4.963 2487
6.0%
4.962 2613
6.3%

nr.employed
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.0533
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2023-09-02T12:55:51.547158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.230334
Coefficient of variation (CV)0.013979019
Kurtosis-0.0047661254
Mean5167.0533
Median Absolute Deviation (MAD)37.1
Skewness-1.0439422
Sum2.1277926 × 108
Variance5217.2212
MonotonicityNot monotonic
2023-09-02T12:55:51.702161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 16234
39.4%
5099.1 8534
20.7%
5191 7759
18.8%
5195.8 3683
 
8.9%
5076.2 1663
 
4.0%
5017.5 1071
 
2.6%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
4963.6 631
 
1.5%
5023.5 172
 
0.4%
ValueCountFrequency (%)
4963.6 631
 
1.5%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
5017.5 1071
 
2.6%
5023.5 172
 
0.4%
5076.2 1663
 
4.0%
5099.1 8534
20.7%
5176.3 10
 
< 0.1%
5191 7759
18.8%
5195.8 3683
8.9%
ValueCountFrequency (%)
5228.1 16234
39.4%
5195.8 3683
 
8.9%
5191 7759
18.8%
5176.3 10
 
< 0.1%
5099.1 8534
20.7%
5076.2 1663
 
4.0%
5023.5 172
 
0.4%
5017.5 1071
 
2.6%
5008.7 650
 
1.6%
4991.6 773
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
36542 
True
4638 
ValueCountFrequency (%)
False 36542
88.7%
True 4638
 
11.3%
2023-09-02T12:55:51.858315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-09-02T12:55:41.930547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:24.525939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:28.250101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.936060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.742064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:33.455064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:35.192065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.824943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:38.639988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.296229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:42.347505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:25.134936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:28.677098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:30.488102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.169064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:33.883067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:35.604064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:37.382941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.056940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.711227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:42.487543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:25.585638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:28.813060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:30.628103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.308026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.025064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:35.738021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:37.522988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.191989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.850225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:42.622553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:25.904593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:28.950065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:30.768060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.453081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.169063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:35.871063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:37.665941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.331945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.984229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:42.756538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:26.332101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.090063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:30.904060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.585063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.310063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.008064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:37.802974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.470991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.117724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:42.904545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:26.659059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.238107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.052059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.732022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.463063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.153023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:37.949985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.615227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.258725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:43.034856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:26.977059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.377061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.184321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:32.867020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.602033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.284026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:38.082989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.746188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.389725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:43.171856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:27.295127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.512059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.322146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:33.005061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.744032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.417021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:38.213982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:39.879185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.519725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:43.304856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:27.613103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.652060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.456149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:33.139069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:34.884055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.549947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:38.351989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.011185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.650725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:43.443902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:27.930061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:29.784060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:31.590160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:33.276022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:35.034065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:36.677985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:38.485988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:40.142231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-02T12:55:41.784505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-02T12:55:51.997362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
agedurationcampaignpdayspreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedjobmaritaleducationdefaulthousingloancontactmonthday_of_weekpoutcomey
age1.000-0.0020.006-0.001-0.0130.0450.0450.1150.0550.0450.2500.2770.1360.1590.0000.0180.1160.1390.0460.1380.196
duration-0.0021.000-0.081-0.0830.042-0.0690.003-0.009-0.078-0.0950.0060.0000.0000.0000.0000.0000.0320.0200.0080.0160.377
campaign0.006-0.0811.0000.055-0.0880.1560.097-0.0010.1410.1440.0000.0000.0020.0170.0220.0210.0640.0470.0180.0470.052
pdays-0.001-0.0830.0551.000-0.5090.2280.057-0.0780.2780.2900.1400.0420.0550.0800.0080.0000.1180.2400.0120.9520.324
previous-0.0130.042-0.088-0.5091.000-0.435-0.283-0.116-0.455-0.4380.0530.0300.0190.0750.0160.0000.2420.1270.0000.7340.236
emp.var.rate0.045-0.0690.1560.228-0.4351.0000.6650.2250.9400.9450.1350.0680.0660.1570.0520.0120.4610.6600.0350.3790.342
cons.price.idx0.0450.0030.0970.057-0.2830.6651.0000.2460.4910.4650.1310.0690.0980.1530.0690.0170.6750.6760.0490.3860.336
cons.conf.idx0.115-0.009-0.001-0.078-0.1160.2250.2461.0000.2370.1320.1090.0730.0640.1380.0400.0110.4170.6000.0450.3680.386
euribor3m0.055-0.0780.1410.278-0.4550.9400.4910.2371.0000.9290.1280.0680.0600.1590.0520.0120.4690.5520.1370.4170.399
nr.employed0.045-0.0950.1440.290-0.4380.9450.4650.1320.9291.0000.1340.0720.0670.1400.0400.0100.5020.6030.0460.4120.410
job0.2500.0060.0000.1400.0530.1350.1310.1090.1280.1341.0000.1840.3590.1520.0110.0100.1280.1100.0160.0990.152
marital0.2770.0000.0000.0420.0300.0680.0690.0730.0680.0720.1841.0000.1160.0950.0090.0000.0720.0500.0110.0370.054
education0.1360.0000.0020.0550.0190.0660.0980.0640.0600.0670.3590.1161.0000.1700.0130.0010.1230.0950.0200.0420.067
default0.1590.0000.0170.0800.0750.1570.1530.1380.1590.1400.1520.0950.1701.0000.0110.0020.1360.1120.0110.0770.099
housing0.0000.0000.0220.0080.0160.0520.0690.0400.0520.0400.0110.0090.0130.0111.0000.7080.0850.0540.0150.0170.009
loan0.0180.0000.0210.0000.0000.0120.0170.0110.0120.0100.0100.0000.0010.0020.7081.0000.0240.0200.0060.0000.000
contact0.1160.0320.0640.1180.2420.4610.6750.4170.4690.5020.1280.0720.1230.1360.0850.0241.0000.6090.0550.2420.145
month0.1390.0200.0470.2400.1270.6600.6760.6000.5520.6030.1100.0500.0950.1120.0540.0200.6091.0000.0670.2420.274
day_of_week0.0460.0080.0180.0120.0000.0350.0490.0450.1370.0460.0160.0110.0200.0110.0150.0060.0550.0671.0000.0140.023
poutcome0.1380.0160.0470.9520.7340.3790.3860.3680.4170.4120.0990.0370.0420.0770.0170.0000.2420.2420.0141.0000.320
y0.1960.3770.0520.3240.2360.3420.3360.3860.3990.4100.1520.0540.0670.0990.0090.0000.1450.2740.0230.3201.000

Missing values

2023-09-02T12:55:43.814856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-02T12:55:44.257855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
040admin.marriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
156servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
245servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
359admin.marriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
441blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
524techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
625servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedunknownunknownnonotelephonemaymon5519990nonexistent1.193.994-36.44.8575191.0no
825servicessinglehigh.schoolnoyesnotelephonemaymon22219990nonexistent1.193.994-36.44.8575191.0no
929blue-collarsinglehigh.schoolnonoyestelephonemaymon13719990nonexistent1.193.994-36.44.8575191.0no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117057retiredmarriedprofessional.coursenoyesnocellularnovthu12469990nonexistent-1.194.767-50.81.0314963.6no
4117162retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117264retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
4117336admin.marrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
4117437admin.marrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
4117529unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4117673retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
4117746blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
4117856retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
4117944technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
024servicessinglehigh.schoolnoyesnocellularaprtue11419990nonexistent-1.893.075-47.11.4235099.1no2
127techniciansingleprofessional.coursenononocellularjulmon33129990nonexistent1.493.918-42.74.9625228.1no2
232techniciansingleprofessional.coursenoyesnocellularjulthu12819990nonexistent1.493.918-42.74.9685228.1no2
335admin.marrieduniversity.degreenoyesnocellularmayfri34849990nonexistent-1.892.893-46.21.3135099.1no2
436retiredmarriedunknownnononotelephonejulthu8819990nonexistent1.493.918-42.74.9665228.1no2
539admin.marrieduniversity.degreenononocellularnovtue12329990nonexistent-0.193.200-42.04.1535195.8no2
639blue-collarmarriedbasic.6ynononotelephonemaythu12419990nonexistent1.193.994-36.44.8555191.0no2
741technicianmarriedprofessional.coursenoyesnocellularaugtue12719990nonexistent1.493.444-36.14.9665228.1no2
845admin.marrieduniversity.degreenononocellularjulthu25219990nonexistent-2.992.469-33.61.0725076.2yes2
947techniciandivorcedhigh.schoolnoyesnocellularjulthu4339990nonexistent1.493.918-42.74.9625228.1no2